Add coverage and change list_equal name

Author: MXB6669

axelrod/result_set.py

@@ -118,9 +118,12 @@ def _reshape_out(
             alternative=0,
         )
 
-        self.wins = self._reshape_two_dim_list(sum_per_player_repetition_df["Win"])
-        self.scores = self._reshape_two_dim_list(sum_per_player_repetition_df["Score"])
-        self.normalised_scores = self._reshape_two_dim_list(normalised_scores_series)
+        self.wins = self._reshape_two_dim_list(
+            sum_per_player_repetition_df["Win"])
+        self.scores = self._reshape_two_dim_list(
+            sum_per_player_repetition_df["Score"])
+        self.normalised_scores = self._reshape_two_dim_list(
+            normalised_scores_series)
 
         self.cooperation = self._build_cooperation(
             sum_per_player_opponent_df["Cooperation count"]
@@ -167,7 +170,8 @@ def _reshape_out(
         self.ranked_names = self._build_ranked_names()
 
         self.payoff_matrix = self._build_summary_matrix(self.payoffs)
-        self.payoff_stddevs = self._build_summary_matrix(self.payoffs, func=np.std)
+        self.payoff_stddevs = self._build_summary_matrix(self.payoffs,
+                                                         func=np.std)
 
         self.payoff_diffs_means = self._build_payoff_diffs_means()
         self.cooperating_rating = self._build_cooperating_rating()
@@ -267,7 +271,9 @@ def _build_good_partner_matrix(self, good_partner_series):
                     # interactions.
                     row.append(0)
                 else:
-                    row.append(good_partner_dict.get((player_index, opponent_index), 0))
+                    row.append(
+                        good_partner_dict.get((player_index, opponent_index),
+                                              0))
             good_partner_matrix.append(row)
         return good_partner_matrix
 
@@ -335,13 +341,15 @@ def _build_normalised_state_distribution(self):
             for counter in player:
                 total = sum(counter.values())
                 counters.append(
-                    Counter({key: value / total for key, value in counter.items()})
+                    Counter(
+                        {key: value / total for key, value in counter.items()})
                 )
             normalised_state_distribution.append(counters)
         return normalised_state_distribution
 
     @update_progress_bar
-    def _build_state_to_action_distribution(self, state_to_action_distribution_series):
+    def _build_state_to_action_distribution(self,
+                                            state_to_action_distribution_series):
         state_to_action_key_map = {
             "CC to C count": ((C, C), C),
             "CC to D count": ((C, C), D),
@@ -397,7 +405,8 @@ def _build_normalised_state_to_action_distribution(self):
         return normalised_state_to_action_distribution
 
     @update_progress_bar
-    def _build_initial_cooperation_count(self, initial_cooperation_count_series):
+    def _build_initial_cooperation_count(self,
+                                         initial_cooperation_count_series):
         initial_cooperation_count_dict = initial_cooperation_count_series.to_dict()
         initial_cooperation_count = [
             initial_cooperation_count_dict.get(player_index, 0)
@@ -412,7 +421,7 @@ def _build_normalised_cooperation(self):
             normalised_cooperation = [
                 list(np.nan_to_num(row))
                 for row in np.array(self.cooperation)
-                / sum(map(np.array, self.match_lengths))
+                           / sum(map(np.array, self.match_lengths))
             ]
             return normalised_cooperation
 
@@ -427,7 +436,8 @@ def _build_initial_cooperation_rate(self, interactions_series):
         with warnings.catch_warnings():
             warnings.simplefilter("ignore")
             initial_cooperation_rate = list(
-                np.nan_to_num(np.array(self.initial_cooperation_count) / interactions_array)
+                np.nan_to_num(np.array(
+                    self.initial_cooperation_count) / interactions_array)
             )
             return initial_cooperation_rate
 
@@ -452,7 +462,8 @@ def _build_eigenmoses_rating(self):
         The eigenmoses rating as defined in:
         http://www.scottaaronson.com/morality.pdf
         """
-        eigenvector, eigenvalue = eigen.principal_eigenvector(self.vengeful_cooperation)
+        eigenvector, eigenvalue = eigen.principal_eigenvector(
+            self.vengeful_cooperation)
 
         return eigenvector.tolist()
 
@@ -576,7 +587,8 @@ def _build_tasks(self, df):
         ]
         sum_per_player_opponent_task = df.groupby(groups)[columns].sum()
 
-        ignore_self_interactions_task = df["Player index"] != df["Opponent index"]
+        ignore_self_interactions_task = df["Player index"] != df[
+            "Opponent index"]
         adf = df[ignore_self_interactions_task]
 
         groups = ["Player index", "Repetition"]
@@ -590,7 +602,8 @@ def _build_tasks(self, df):
         groups = ["Player index"]
         column = "Initial cooperation"
         initial_cooperation_count_task = adf.groupby(groups)[column].sum()
-        interactions_count_task = adf.groupby("Player index")["Player index"].count()
+        interactions_count_task = adf.groupby("Player index")[
+            "Player index"].count()
 
         return (
             mean_per_reps_player_opponent_task,
@@ -610,7 +623,8 @@ def __eq__(self, other):
             other : axelrod.ResultSet
                 Another results set against which to check equality
         """
-        def list_equal(v1: List[float], v2: List[float]) -> bool:
+
+        def list_equal_with_nans(v1: List[float], v2: List[float]) -> bool:
             """Matches lists, accounting for NaNs."""
             if len(v1) != len(v2):
                 return False
@@ -640,8 +654,10 @@ def list_equal(v1: List[float], v2: List[float]) -> bool:
                 self.cooperating_rating == other.cooperating_rating,
                 self.good_partner_matrix == other.good_partner_matrix,
                 self.good_partner_rating == other.good_partner_rating,
-                list_equal(self.eigenmoses_rating, other.eigenmoses_rating),
-                list_equal(self.eigenjesus_rating, other.eigenjesus_rating),
+                list_equal_with_nans(self.eigenmoses_rating,
+                                     other.eigenmoses_rating),
+                list_equal_with_nans(self.eigenjesus_rating,
+                                     other.eigenjesus_rating),
             ]
         )
 
@@ -711,7 +727,8 @@ def summarise(self):
             rates = []
             for state in states:
                 counts = [
-                    counter[(state, C)] for counter in player if counter[(state, C)] > 0
+                    counter[(state, C)] for counter in player if
+                    counter[(state, C)] > 0
                 ]
 
                 if len(counts) > 0:
@@ -734,7 +751,8 @@ def summarise(self):
 
         summary_data = []
         for rank, i in enumerate(self.ranking):
-            data = list(summary_measures[i]) + state_prob[i] + state_to_C_prob[i]
+            data = list(summary_measures[i]) + state_prob[i] + state_to_C_prob[
+                i]
             summary_data.append(self.player(rank, *data))
 
         return summary_data

axelrod/tests/unit/test_resultset.py

@@ -195,6 +195,23 @@ def _clear_matrix(self, matrix):
             for j in range(len(matrix[i])):
                 matrix[i][j] = 0
 
+    def test_ne(self):
+        rs_1 = axelrod.ResultSet(
+            self.filename,
+            self.players,
+            self.repetitions
+        )
+
+        rs_2 = axelrod.ResultSet(
+            self.filename,
+            self.players,
+            self.repetitions
+        )
+
+        rs_2.wins = -1
+
+        self.assertNotEqual(rs_1, rs_2)
+
     def test_nan_vectors(self):
         rs_1 = axelrod.ResultSet(
             self.filename,